Version 3.1
The aim of this hypothesis is to investigate the correlation between Natural Rate of Rise (NRR) and various capital programmes. DMAs have been classified based on changes in Nightflow data over time, namely Jumper (sudden increase or decrease over time), Creeper (prolongued and steady increase or decrease over time) and Stable (little change and variance over time).
This nightflow data has been compared with capital programmes, including Mains Replacement; at the time of writing, there is a small amount of evidence to suggest that leakage increases can be suppressed by increasing the volume of mains replacement, although this is inconclusive; further investigation is recommended to prove or disprove this.
In addition, Nightflow data has been correlated with Leakage Detection Effort; the findings from this suggested that, while a large amount of effort was focussed on those DMAs which later reported a high leakage increase, there is otherwise some evidence to suggest that there is a relationship between increased labour hours and a relative suppression of increase in leakage.
Furthermore, the available Nightflow data has been correlated with Universal Metering Programme (UMP) data, in order to begin investigations into the hypothesis that this programme affected leakage in addition to reducing short term demand. No correlation has yet been found, although the most useful data is not yet available - at the time of writing, Nightflow data is not available prior to 2015.
It is hypothesised that detection efforts, and a number of capital programmes, have an effect on leakage. This experiment is therefore linked to experiments in Hypothesis 2, in addition to those mentioned below, in seeking to determine possible correlations between these programmes and leakage. As such, these experiments are also included in this report.
This experiment takes the results of Experiment 4.1 and compares these with historic mains replacement data, in order to seek to determine the relationship between these.
This experiment takes the results of Experiment 4.1 and compares these with historic leakage detection effort, in order to seek to determine the relationship between these.
Leakage Detection Effort is also compared with the material compositions of DMAs, as derived in Experiment 3.1.
This experiment takes the results of Experiment 4.1 and compares these with meter installation dates, in order to seek to determine the relationship between the UMP, leakage and consumption.
The following datasets are used in the investigation of this hypothesis:
This data was sourced from the Nightflow Mastersheet Workbook, detailing monthly Nightflow volumes from 2015 to 2018 inclusive.
The first stage of this analysis is to statistically classify DMAs. The categories chosen are Jumper (sudden increase or decrease over time), Creeper (prolongued and steady increase or decrease over time) and Stable (little change and variance over time).
A small amount of cleansing is carried out on this data; a value of zero is assumed to indicate no data, and values greater than two times, or less than half, the mean value over four years for the same DMA are assumed to be anomalous and excluded from the analysis.
To eliminate seasonal variance from the analysis, moving averages are then derived for each 12 month period within the analysis. These moving averages are then compared to the overall mean nightflow for the same DMA.
The leakage change over time is analysed statistically and tested for significance; DMAs are identified as creepers when a consistent change is observed, which could either be an increase or a decrease.
Jumper DMAs are currently identified as those where there exist a period of 6 months in which 5 of these record a moving average of monthly nightflow less than 0.8 times the overall DMA average, followed by an increase over 2 months, after which 5 of the following 6 months record a moving average of monthly nightflow more than 1.2 times the overall DMA average. Using the same method in reverse, Jumper DMAs in which a relatively sudden decrease has taken place are identified.
There is no evidence to suggest, using this method of identifying Jumper DMAs, that the ‘Beast from the East’ extreme weather event in early 2018 affected any particular DMA in this manner. Instead, it is likely that the rise in leakage observed by Southern Water at this time has been more generally spread across the network.
DMAs with no more than 5% change, from the 2015 to the 2018 average nightflow, and with less than 12 months in this period more than 20% either side of the 4-year average nightflow, are classified as Stable.
DMAs in which none of the above effects are observed are classified as Other. Collectively, these account for an increase in Nightflow of approximately 15%, similar to the company-wide increase of 16% over the same period.
Where there is incomplete data over the four year period, the DMA is classified as having Insufficient Data.
An example of nightflow readings over time for one DMA in each of these categories is displayed below.
The distribution of DMAs into these categories is as follows.
The map below provides a graphical visualisation of DMAs according to the categories identified above.